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In this paper, we aim to evaluate the potential of mixed models in assessing the impact of ibuprofen, fluoxetine, and a mix of them, on the Gosner stage development of Bufo bufo tadpoles, using incomplete trajectory data. We consider the mixed effects models as a good choice to deal with missing data, the within-subject correlations over time, and the feasibility to model time as a continuous covariate. We fitted several models with different random and fixed effects structures and compared them using the Likelihood Ratio (LR) test and Akaike Information Criteria (AIC). Influential analysis and multiple imputation are also performed to check the stability of the results. Mixed-effects models successfully modeled the Gosner stage incomplete longitudinal trajectories. Based on all fitted models, a significant difference in the evolution of the Gosner stage is observed between the group treated with a mix of pharmaceuticals and the other three groups (i.e., ibuprofen, fluoxetine, control).

To determine if people infected with SARS-CoV-2 were at higher risk of developing selected medical conditions post-recovery, data were extracted from the database of a large health maintenance organization (HMO) in Israel between March 2020 and May 2021. For each condition, a condition-naïve group prior to COVID-19 (PCR-positive) infection were compared to a condition-naïve, non-COVID-19 infected group, matched by gender, age, socioeconomic status, minority group status and number of months visited primary care physician (PCP) in previous year. Diagnosis and recuperation dates for each COVID-19 infected participant were applied to their matched comparison participant (1:1 ratio). Incidence of each condition was measured between date of recuperation and end of study period for each group and Cox regression models developed to determine hazard ratios by group status, controlling for demographic and health variables. Crude and adjusted incidence rates were higher for the COVID-19 infected group than those not infected with COVID-19 for treatment for depression/anxiety, sleep disturbance, diagnosis of deep venous thrombosis, lung disease and fibromyalgia. Differences in incidence were no longer observed between the two groups for treatment of sleep disturbance, and diagnosis of lung disease when those hospitalized during the acute-phase of illness (any reason) were excluded. No difference was found by COVID-19 infection status for post-acute incidence of diabetes, cerebrovascular accident, myocardial infarction, acute kidney disease, hypertension and ischemic heart disease. Patients post-COVID-19 infection should be evaluated for depression, anxiety, sleep disturbance, DVT, lung disease and fibromyalgia.

To maximize safety and cost-efficiency of CO2 storage, it is necessary to understand how a high permeability in the near-well region is maintained. Nucleation and growth of salt crystals driven by the evaporation of formation water into under-saturated (dry) super-critical CO2 stream results in the changes in the porosity and permeability of the near well-bore area. Permeability reduction is the main reason of the injectivity loss in the context of CO2 storage in saline aquifers. According to recent studies, during CO2 storage, salt crystals form in two different forms: 1) single, large crystals in the aqueous phase, and 2) aggregates of micrometer size salt crystals in the gas phase. All the previous numerical studies at pore-scale addressed the formation of single, large crystals in the aqueous phase. In this work, we developed a 3D pore-scale reactive transport solver (a D3Q19 advection-diffusion Lattice-Boltzmann model) and took nucleation process into account via a new probabilistic approach to simulate the formation of micro-meter size salt crystals in the gas phase and their effect on changes in pore morphology and permeability for the first time. Comparing the results of porosity-permeability relation with some of the well-known clogging models, confirms the need for a new clogging model to capture the permeability reduction caused by salt aggregates.

Aims: to examine the effect of cognitive-behavioral training combined with motivational interviewing on severty of depression in patients with diabetes and depressive symptoms. Methods: This clinical trial was performed on two groups of 45 patients (n = 90) with type 2 diabetes and depression in a diabetes clinic affiliated with a teaching hospital in Iran in 2021. The participants were selected using convenience sampling and randomly divided into two intervention groups. The patients in the cognitive-behavioral intervention group with motivational interviewing attended 8 face-to-face training sessions 3 times a week, and the patients in the cognitive-behavioral group attended only 4 cognitive-behavioral training sessions 2 times a week. Twelve weeks after the intervention, the severty of depression symptoms were collected using Beck Depression Inventory (BDI). The collected data were analyzed with SPSS software (version 22) using paired samples t-test, independent samples t-test and chi-square. Results: The results showed that before the intervention there was no difference between the two groups in terms of mean depression score (p=0.54).But after receiving the intervention the mean and standard deviation of depression score of the patients in the group receiving cognitive-behavioral training combined with motivational interviewing (20.24±4.40) were significantly lower than the corresponding values for the patients in the cognitive-behavioral training group (24.62±4.26, p = 0.001). Conclusion: Cognitive-behavioral training combined with motivational interviewing had a greater effect on decreasing depression Severity in patients with diabetes and depression compared to cognitive-behavioral training alone. Thus, motivational approaches can be incorporated into routine psychological training to better manage diabetes and depression symptoms. Trial Registration Details: This study was a clinical trial approved with the code IRCT20160924029954N14. Funding Information: This study did not receive any specific grant from the funding agencies in the public, commercial, or not-for-profit sectors. Declaration of Interests: The authors declare no conflicts of interest in this study. Ethics Approval Statement: This study was approved by the Ethics Committee of Zahedan University of Medical Siences with the Ethic code of IR.ZAUMS.REC.1399.190.

Mass surrenders—spiking surrenders of life insurance policies within a short period— are widely documented prior to many life insurers failures, but their impacts on insurers are rarely quantified. This paper builds a model of mass surrenders in a heterogeneous pool consisting of a minority of financially guided policyholders and a majority of individual policyholders with contagious behavior that can be triggered contingent on past surrenders becoming salient. By incorporating it into a life insurance pricing framework, we study mass surrenders’ impacts on contract valuation and early bankruptcy of the insurer, allowing for its structural default under regulatory solvency intervention. Our numerical results show that while contagion aligns individual policyholders’ surrender behavior with the optimal surrender of financially guided ones, it jeopardizes their financial positions in favor of equity holders. Surprisingly, contagion at worst only marginally enlarges the insurer’s bankruptcy rate, which is not driven by strict solvency regulation. Insurers have incentives to encourage contagion. Despite its public perception of endangering market stability as similar to a bank run, surrender contagion does not threaten insurers’ solvency, but can improve it instead with the assets left behind by individual policyholders.

In a full-scale CCS, millions of tons of CO2 must be stored underground. Injection of dry or undersaturated (with respect to water) CO2 leads to dry-out of formation water and salt precipitation. Salt precipitation during CO2 injection into the geological formations causes reduced injectivity and negatively influences reservoir rock properties. It also may have the potential to block CO2 leakage pathways within the fractured caprocks. The present-day reservoir-scale models of salt precipitation consider mechanisms such as water evaporation into CO2 and capillary backflow of water into the dried zone. However, it has been suggested that salt precipitation due to these mechanisms fills only a fraction of the pore network and does not significantly impact the permeability. We report microfluidic experiments on glass-microchips and organic-rich shale specimens to provide insights into the physics and dynamics of salt precipitation at pore-scale and to find the possible explanations for the large-scale salt precipitation observed in the field operations. Moreover, we investigate whether salt crystals can partially or entirely clog fracture apertures and leaking pathways in the seal sequences. The experimental results introduce two interrelated phenomena –self-enhancing of salt growth and water film salt transport, which together remarkably intensify the rate and amount of precipitations. It is shown that salt crystals, although at different rates, grow in both aqueous and gas phases. The salt crystals precipitate in two distinct forms: (a) large single cubic halite crystals in the aqueous phase and (b) dense micrometer-sized halite crystals on the interface of rock and CO2 stream. The micrometer-sized crystals in the gas phase create a microporous medium with large capillarity that can strongly imbibe brine over long distances to the evaporation front via capillary connected water films. It is demonstrated that the CO2 phase states influence magnitude, distribution and precipitation patterns of salt accumulations.

This material is based upon work supported by the National Science Foundation (SES-1423944, PI: Daniel Pemstein), Riksbankens Jubileumsfond (Grant M13-0559:1, PI: Staffan I. Lindberg), the Swedish Research Council (2013.0166, PI: Staffan I. Lindberg and Jan Teorell), the Knut and Alice Wallenberg Foundation (PI: Staffan I. Lindberg), and the University of Gothenburg (E 2013/43); as well as internal grants from the Vice-Chancellor’s office, the Dean of the College of Social Sciences, and the Department of Political Science at University of Gothenburg. Marquardt acknowledges research support from the Russian Academic Excellence Project ‘5-100.’ We performed simulations and other computational tasks using resources provided by the Notre Dame Center for Research Computing (CRC) through the High Performance Computing section and the Swedish National Infrastructure for Computing (SNIC) at the National Supercomputer Centre in Sweden (SNIC 2016/1-382, SNIC 2017/1-406 and 2017/1-68). We specifically acknowledge the assistance of In-Saeng Suh at CRC and Johan Raber and Peter Mu nger at SNIC in facilitating our use of their respective systems.

In developing countries, NGOs and governments often rely on local community-based groups for the delivery of financial and public services. This paper provides causal evidence of how the design of rules used for group leader selection affects leader identity and shapes group service delivery. In collaboration with the NGO BRAC, we randomly assigned newly-formed Savings and Loan Groups to select their leaders using either (i) a procedure in which final outcomes are decided in a public discussion and (ii) a procedure in which final outcomes are decided in a private vote. Leaders selected with a private vote are found to be less positively selected on socioeconomic characteristics than those elected in the public procedure and at the same time more representative of regular group members. Furthermore, selecting more representative leaders — through a private vote — results in groups that are more inclusive towards poor members by giving them more credit and retaining them longer. Three years after their creation, vote groups are more inclusive than discussion groups, without becoming less economically efficient.